CN117034505B

CN117034505B - Automatic dimension marking method for parameterized assembly design of three-dimensional mold structure

Info

Publication number: CN117034505B
Application number: CN202311294601.3A
Authority: CN
Inventors: 常利芳
Original assignee: Shenzhen Changfeng Laser Knife Mould Co ltd
Current assignee: Shenzhen Changfeng Laser Knife Mould Co ltd
Priority date: 2023-10-09
Filing date: 2023-10-09
Publication date: 2024-01-26
Anticipated expiration: 2043-10-09
Also published as: CN117034505A

Abstract

The invention relates to the technical field of computer aided design, in particular to an automatic dimension marking method for parameterized assembly design of a three-dimensional die structure. The method comprises the following steps: acquiring and analyzing the die structure data to acquire die structure analysis data; modeling the die structure analysis data to obtain a parameterized die model; constructing an automatic dimension marking model according to the mould structure data; marking the parameterized mold model to obtain automatic marking size data; acquiring mould production size data, marking a parameterized mould model, acquiring actual marked size data, and carrying out size error self-adaptive correction to acquire adjustment marked size data; and acquiring die demand data, and carrying out parameterized assembly design according to the die demand data, the parameterized die model and the adjustment marking size data to acquire the die assembly model. The invention utilizes a parameterization method to automatically size label the three-dimensional mould structure.

Description

Automatic dimension marking method for parameterized assembly design of three-dimensional mold structure

Technical Field

The invention relates to the technical field of computer aided design, in particular to an automatic dimension marking method for parameterized assembly design of a three-dimensional die structure.

Background

In the field of mold manufacturing, the design and assembly of three-dimensional models is a critical step. These models often contain multiple components and complex structures, and their size and position must be precisely controlled to ensure quality and performance of the final product. Traditionally, in three-dimensional mold designs, engineers have been required to manually label and resize each component, a tedious and error-prone task. In addition, if the mold needs to be modified or optimized, it must be re-sized, which consumes a lot of time and manpower resources.

Disclosure of Invention

Accordingly, the present invention is directed to an automatic sizing method for parameterized assembly design of three-dimensional mold structures, which solves at least one of the above-mentioned problems.

In order to achieve the above purpose, an automatic dimension marking method for parameterized assembly design of a three-dimensional mold structure comprises the following steps:

step S1: acquiring die structure data, and performing die structure analysis according to the die structure data so as to acquire die structure analysis data;

step S2: carrying out parameterized modeling on the die structure analysis data so as to obtain a parameterized die model;

Step S3: extracting die size information from the die structure analysis data, thereby obtaining die size data; detecting turning points of the mold structure data to obtain mold turning point data, and constructing an automatic size marking model according to the mold size data and the mold turning point data;

step S4: performing design dimension fit labeling on the parameterized mold model by using the automatic dimension labeling model so as to obtain automatic dimension labeling data;

step S5: acquiring mould production size data, carrying out production size fit marking on a parameterized mould model by utilizing the mould production size data so as to obtain actual marking size data, and carrying out size error self-adaptive correction on automatic marking size data according to the actual marking size data so as to obtain adjustment marking size data;

step S6: and acquiring die demand data, and carrying out parameterized assembly design according to the die demand data, the parameterized die model and the adjustment marking size data, so as to acquire the die assembly model.

The invention can provide more comprehensive and accurate mold information by acquiring the mold structure data and analyzing the mold structure. This allows the production team to better understand the structure and function of the mold and provide the necessary basis for subsequent operations. Parameterized modeling of the mold structure analysis data can create reusable mold models. Such a model can be reused across multiple projects, thereby saving time and resources. Meanwhile, the parameterized die model can be conveniently modified and optimized to adapt to different product requirements. By extracting the die size information and detecting the die turning point data, more accurate die size data and position information of important feature points can be provided. This helps to understand the size and structural features of the mold to better perform mold assembly and adjustment. The automatic dimension marking model is utilized to carry out design dimension fit marking on the parameterized die model, and dimension marking can be automatically added for the die. This simplifies the production team effort and reduces the amount of manual labeling effort and the risk of errors. The production operator can obtain the size information of the mould more quickly and accurately know the size parameters of the components. The dimension in actual production can be fed back into the mould model by acquiring the mould production dimension data and applying the mould production dimension data to the production dimension fit marking of the parameterized mould model. This allows the production team to know the actual dimensions of the mold accurately and make the necessary adjustments and optimizations to ensure the quality of the final product. And carrying out parameterized assembly design according to the die demand data, the parameterized die model and the adjustment marking size data, so that the rapid assembly and adjustment of the die can be realized. This enables the production team to more quickly perform the mold assembly and make the necessary adjustments according to the actual needs. This improves the production efficiency and ensures that the mould can meet the product requirements.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of the steps of the automatic dimension marking method for parameterized assembly design of a three-dimensional mold;

FIG. 2 is a detailed step flow chart of step S1 of the present invention;

FIG. 3 is a detailed step flow chart of step S2 of the present invention;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides an automatic sizing method for parameterized assembly design of a three-dimensional mold structure, the method comprising the following steps:

in this embodiment, structural data of the mold is collected and obtained, including information about the component configuration, connection mode, critical dimensions, etc. of the mold. The mold structure data is loaded using specialized mold design software or CAD software. And (3) carrying out die structure analysis, and considering factors such as rigidity, strength, stability and the like of the die. Structural analysis may be performed using a Finite Element Analysis (FEA) method to determine the optimal design of the mold. And analyzing the structural strength, deformation and stress of the related mold by analyzing the mold structure.

in this embodiment, the modeling of the model structure analysis data is performed using parametric modeling software, such as a parametric modeling tool in CAD software. Parameters such as size, shape and the like in the data are analyzed according to the mold structure, and the parameters are converted into a parameterized mold model, namely, the mold is constructed by using the model with adjustable parameters. The parameterized modeling can enable the design of the die to be more flexible and adjustable, and is convenient for subsequent dimension marking and adjustment.

in this embodiment, the size information of the mold, including the length, angle, etc., of the straight line segment is extracted from the mold structure data. And detecting turning points of the die structure data, namely detecting inflection points or abrupt points in the die for subsequent dimension marking and self-adaptive adjustment.

In this embodiment, an automatic dimension marking model is constructed based on the mold dimension data and the turning point data. And (3) carrying out design dimension fit marking on the parameterized die model by using an automatic dimension marking model, namely automatically marking the dimension data of the die.

in this embodiment, mold production dimension data, that is, mold dimension data measured in an actual production process, is obtained. And carrying out production dimension fit marking on the parameterized mold model by using mold production dimension data to obtain actual marking dimension data. And comparing the difference between the actual size-marked data and the automatic size-marked data, and carrying out size error self-adaptive correction according to the difference, namely adjusting the automatic size-marked data to be better matched with the actual size-marked data.

In this embodiment, the mold demand data is obtained, including the environment in which the mold is used, the function demand, the assembly requirement, and the like. And carrying out parameterized assembly design according to the die demand data, the parameterized die model and the adjustment marking size data. And (3) performing assembly design of the die by using CAD software or die design software, including arrangement, connection mode, assembly sequence and the like of the parts, so as to obtain a final die assembly model.

Optionally, step S1 specifically includes:

step S11: acquiring mold structure data, and performing image extraction and material data extraction on the mold structure data so as to acquire a mold structure image set and mold material data;

in this embodiment, the mold structure data is collected and obtained, including a design drawing of the mold, a CAD file, or other form of structure description. And processing the mold design drawing or CAD file, extracting image information related to the mold structure, and identifying the geometric shape, the part connection relation and the like in the mold drawing by using an image processing algorithm. Meanwhile, material information of the mold is extracted from a mold design drawing or a CAD file, and related data such as material names, performance parameters and the like are extracted by using a text recognition algorithm.

Step S12: performing background segmentation on the mold structure image set so as to obtain a structure foreground image set;

in this embodiment, the background segmentation is performed on the mold structure image set using an image processing technique to separate the foreground and background of the structure object. Common background segmentation methods include threshold segmentation, edge detection, region-based segmentation, and the like. After the background is segmented, a foreground image set of the mold structure, i.e. an image comprising only the mold parts, is obtained.

Step S13: constructing a three-dimensional model of the mold according to the mold structure image set, thereby obtaining a three-dimensional mold model;

in this embodiment, the model is constructed in three dimensions using computer vision and three-dimensional reconstruction techniques based on the foreground image set. The three-dimensional point cloud data of the mold can be obtained by using methods such as structured light scanning, stereoscopic vision and the like. And processing the point cloud data by using point cloud processing software or three-dimensional modeling software to generate a three-dimensional model of the die.

Step S14: performing geometric analysis on the three-dimensional mold model so as to obtain mold geometric analysis data;

in this embodiment, geometric analysis is performed on the three-dimensional mold model, including analysis in terms of size, shape, curvature, and the like. Geometric analysis may be performed using Computer Aided Design (CAD) software or three-dimensional modeling software. The data of the size parameters, the geometric properties and the like of the die can be obtained according to the requirements.

Step S15: performing material analysis on the three-dimensional mold model by using mold material data, thereby obtaining mold material analysis data;

in this embodiment, the previously extracted mold material data is applied to a three-dimensional mold model for material analysis. Simulation and analysis of material properties may be performed using material analysis software or finite element analysis software. And acquiring performance characteristic data of the die under different material conditions according to the die material data and the simulation result.

Step S16: and carrying out data combination on the die geometric analysis data and the die material analysis data so as to obtain die structure analysis data.

In this embodiment, the geometric analysis data and the material analysis data of the mold are combined to form comprehensive mold structure analysis data. The geometric data and the material data may be correlated to form geometric-material relationships for different portions of the mold. The combined mold structure analysis data includes geometric features, material properties, performance indexes, etc. of the mold and can be used for subsequent mold design and analysis.

The invention can acquire the die structure image set comprising various angles and components through image extraction. Such an image set provides a detailed visual reference so that the production team can better understand the shape, component layout and structural details of the mold. By material data extraction, information about the material used for the mold, such as material type, density, hardness, etc., can be obtained. This provides the basis for subsequent mold analysis and design, which ensures that the appropriate materials are selected during the mold manufacturing process. Background segmentation may separate the background from the foreground in the mold structure image, thereby extracting a set of foreground images that contain only the mold structure. This makes the specific shape and structure of the mold more clearly visible, facilitating subsequent mold modeling and analysis. Background segmentation can reduce clutter and interference in images, making subsequent image processing and analysis more efficient and accurate. According to the mold structure image set, three-dimensional model construction can be performed to generate a mold model with accurate shape and structure. This allows the production team to better visualize and manipulate the mold in a virtual environment, doing mold design, assembly, and analysis. By constructing a three-dimensional mold model, the true shape of the mold can be restored from the image data, thereby providing a basis for subsequent geometric analysis and material analysis. Geometric analysis can extract various geometric features of the mold, such as dimensions, curved shape, curvature, etc. The data provides important basis for subsequent mold assembly, adjustment and optimization, and ensures that the geometric performance of the mold meets the requirements. Geometric analysis can help detect and identify problems in mold design, such as dimensional deviations, hole site offsets, and the like. By finding and solving these problems early, quality problems and delays in actual production can be avoided. Through material analysis, the properties of the materials used for the die, including strength, wear resistance, corrosion resistance, and the like, can be evaluated. This helps to select the appropriate material to ensure good performance and durability of the mold during use. The material analysis may provide information about the life and durability of the mold material. This is very valuable in making a mold maintenance plan, measuring mold life, predicting replacement timing, and the like. The data consolidation may integrate and integrate the results of the geometric analysis and the material analysis, providing more comprehensive mold structure analysis data. This allows the production team to integrate the geometric features and material properties of the mold to better understand the performance and potential problems of the mold. Through comprehensive analysis results, some contradictions and conflicts possibly existing in the die design can be found and solved, so that the structure and the performance of the die are further improved.

Optionally, step S14 specifically includes:

step S141: performing size measurement on the three-dimensional mold model, thereby obtaining mold size data;

in this embodiment, dimensional measurement is performed on each geometric feature of the mold using three-dimensional model measurement software or CAD tools. The dimensions of the length, width, height, etc. of the mould can be measured by selecting a specific measuring tool, such as a measuring line, a measuring curve, a measuring surface, etc. The measurement results may be presented in digital, graphical or reported form, providing die size data.

Step S142: extracting geometric elements according to the three-dimensional mold model, so as to obtain mold geometric elements;

in this embodiment, the geometric elements of the mold are extracted according to the three-dimensional mold model using CAD tools or special geometric element extraction software. Geometric elements may include straight lines, curved lines, arcs, curved surfaces, etc., which define the geometry of the mold. The extracted geometric elements can be used for the following steps of geometric relation analysis, topological relation extraction and the like.

Step S143: performing digital conversion on the three-dimensional mold model so as to obtain a digital mold model;

in this embodiment, a three-dimensional scanner or a structured light scanner is used to scan an actual mold object, so as to obtain three-dimensional point cloud data of the mold object. And importing the point cloud data into three-dimensional point cloud processing software, such as MeshLab or Geomagic, and the like, and processing and reconstructing the point cloud data to generate a digital mold model. The digital mold model can be used for the subsequent steps of topological relation extraction, geometric relation analysis and the like.

Step S144: extracting topological relation from the digital mold model, so that mold topological relation data are obtained;

in this embodiment, a topological relation extraction algorithm is used to process the digital mold model and extract the topological structure information of the mold. The topological relationships may include connection relationships between mold parts, interface constraints, and the like. The extracted topological relation data can be used for the following steps of geometric relation analysis, die surface structure extraction and the like.

Step S145: carrying out geometric relation analysis on the three-dimensional mold model according to the mold topological relation data and the mold geometric elements, thereby obtaining mold geometric relation data;

in this embodiment, the geometric relationship between the mold elements is analyzed in combination with the geometric element and the topological relationship data of the mold. The geometric relationships of the distance, the included angle, the relative position and the like among the die elements can be calculated. The geometric relationship data can be used for optimization of mold design, planning of assembly process, and the like.

Step S146: extracting a mold surface structure of the three-dimensional mold model, thereby obtaining mold surface structure data;

in this embodiment, surface structure information is extracted from a three-dimensional model of a mold by using three-dimensional model analysis software or a curved surface extraction algorithm. The surface structures may include features such as relief surfaces, indentations, holes, etc. of the mold. The extracted mold surface structure data can be used for subsequent curved surface analysis, mold geometric analysis and the like.

Step S147: performing surface analysis on the mold surface structure data to obtain mold surface analysis data;

in this embodiment, curved surface analysis software or CAD tool is used to analyze and evaluate the curved surface of the mold. The indexes such as curvature, curvature change, curve error and the like of the curved surface can be calculated. The curved surface analysis data can be used for precision control of die design, optimization of die processing and the like.

Step S148: and carrying out data combination on the die curved surface analysis data, the die size data and the die geometric relation data, thereby obtaining the die geometric analysis data.

In this embodiment, the mold surface analysis data, the dimension data, and the geometric relationship data are integrated and combined. The information of the different data sources may be integrated together using data processing and fusion tools. The combined die geometry analysis data includes dimensional information, geometric relationships, surface structure features, etc. for die design, fabrication, and optimization.

According to the invention, by measuring the dimension of the three-dimensional mould model, the accurate dimension information of each part of the mould can be obtained. The method is very important to links such as process planning, size verification and assembly in the mould manufacturing process, and ensures that the size of the mould meets the design requirement. By dimension measurement, the dimensional change of each part of the die can be known, and dimensional deviation, defects or inconsistent places can be found. This facilitates die analysis and can provide improved and optimized orientation to improve die accuracy and reliability. Important geometric elements in the mold model, such as holes, edges, concave-convex surfaces and the like, can be identified and extracted through geometric element extraction. This provides a detailed understanding of the mold structure, facilitating subsequent analysis and operation. Geometric element extraction provides basis for design improvement of the die. By analyzing the extracted geometric elements, potential design issues or optimization opportunities can be identified and targeted adjustments made to meet specific production requirements. Through digital conversion, the solid mold model can be converted into a digital model, i.e., the physical mold into a computer-processable data representation. This allows for more flexible, efficient and accurate mold analysis, design and simulation in a computer environment. The digitized mold model can be iterated and modified without modification on the actual physical mold. This can speed up design and improvement and reduce cost, thereby improving production efficiency and quality. The topological relation extraction can identify and extract the connection relation between all the components in the die model, such as connection of holes, matching relation of parts and the like. This helps to analyze the dependency in the assembly process and design of the mold, ensuring proper assembly and reliability of the mold. Through the topological relation extraction, the relative positions and constraint relations among the various components in the die can be determined. This aids in the assembly process of the mold and supports adjustment and optimization of the mold, ensuring stability and performance of the mold. The geometric analysis may reveal relative positions, containment relationships, and spatial constraints between the different mold parts. This contributes to the accuracy and stability of the mold assembly. The geometric relationship analysis may help detect and identify poor geometric relationships in the mold, such as excessive gaps, collisions, and the like. By analyzing and adjusting the geometric relationships, the problems of the die can be repaired, and the performance and the accuracy of the die are improved. The extraction of the mold surface structure can capture detailed information of the mold surface, such as texture, concave-convex, wall thickness, processing characteristics and the like. This provides a comprehensive understanding of the surface quality and characteristics of the mold, facilitating subsequent surface treatments and process planning. By analyzing the extracted mold surface structure, surface defects, non-uniformities, and quality problems can be identified. This helps to improve the surface quality of the mold and to improve the appearance and performance of the product. The surface analysis may provide an assessment of geometric characteristics such as curvature, shape, and smoothness of the mold surface. This helps to detect whether the curved surface meets design requirements and allows for curved surface adjustment and correction as needed. The surface analysis can identify defects, deformations, distortions, etc. in the mold surface. By finding and solving these problems early, the manufacturing quality and performance of the mold can be improved. The data combination can integrate the die curved surface analysis data, the die size data and the die geometric relationship data to provide comprehensive die geometric characteristics. This helps to evaluate and optimize the overall performance and feasibility of the mold. Through data merging, various analysis data can be integrated into a comprehensive die geometric analysis data set, and visual display can be carried out. Such visual results provide more intuitive and comprehensive die analysis conclusions, facilitating decisions and improving the die design and manufacturing process.

Optionally, step S15 specifically includes:

calculating the material limit according to the mold material data, so as to obtain material strength parameter data;

in this example, the limit calculation was performed using the theory of material mechanics based on the physical and mechanical properties of the mold material. For example, the modulus of elasticity, yield strength, tensile strength and fracture toughness of the material are determined. This can be achieved by laboratory tests or literature studies. Once these parameters are obtained, a constitutive model of the mold material can be built up for stress analysis in a subsequent step.

Acquiring actual working condition data of a die;

in this embodiment, in order to obtain actual working condition data of the mold, a sensor and a data recording device are required to be installed on the mold to monitor working conditions. This may include measuring parameters such as temperature, pressure, vibration and displacement of the mold. These data may be obtained by continuous monitoring and recording to ensure that changes in the mold under different conditions are captured.

Carrying out boundary condition analysis according to the actual working condition data of the die and the material strength parameter data, thereby obtaining material boundary conditions;

in this embodiment, the boundary condition of the mold is determined by analyzing the actual working condition data. For example, if the mold is operated in a high temperature environment, boundary conditions may relate to temperature distribution and heat conduction. If the mould is subjected to an external load, the boundary conditions will relate to the magnitude and direction of the load. These conditions will be used in subsequent finite element analysis to simulate the stress conditions of the mold.

Carrying out loading condition analysis according to the actual working condition data of the die and the material strength parameter data, thereby obtaining material loading conditions;

in this embodiment, the loading condition analysis is performed according to the actual working condition data and the strength parameter of the material, which involves analyzing the external loading of the mold under the actual working condition. This includes static loading, dynamic loading, or transient loading. By combining the operating mode data with the material strength parameters, loading conditions, including load size, frequency, time variation, etc., can be determined. These loading conditions will be used to simulate the stress conditions of the mold in operation.

Carrying out stress analysis on the three-dimensional mold model by utilizing material boundary conditions and material loading conditions so as to obtain material stress distribution data;

in this embodiment, the boundary conditions and loading conditions are applied to the three-dimensional mold model using finite element analysis or other numerical simulation methods. By solving the stress-strain equation, stress distribution data of each point in the die can be obtained. These data will reveal stress distribution of the mold under different conditions, which helps to determine the stress location and potential stress concentration area.

Performing deformation simulation on the three-dimensional mold model according to the material loading condition, so as to obtain material deformation data;

In the deformation simulation in this embodiment, the three-dimensional mold model is analyzed according to the loading conditions to determine the deformation condition of the mold. This includes parameters such as displacement, strain and deformation of the mould. By simulating the deformation of the mould in operation, the stability and deformation degree of the structure can be evaluated, so that the design is further optimized.

And carrying out data combination on the material stress distribution data and the material deformation data so as to obtain die material analysis data.

In this embodiment, the stress distribution data and the deformation data of the material are integrated and combined. Information from different data sources may be integrated and combined using data processing and analysis tools. The combined mold material analysis data may include stress versus deformation, material fatigue life, stress concentration areas, etc. for mold design, material selection, and performance optimization.

According to the invention, the ultimate strength of the die material is calculated, so that the bearing capacity and the deformation resistance of the material in the ultimate state can be determined, and basic data is provided for the design of the die. This helps ensure that the mold does not fail or fail during use, improving the reliability and service life of the mold. The actual working condition data of the die are obtained to simulate and analyze the loading and boundary conditions born by the die in the actual use process. Such data may include information about the temperature, pressure, flow rate, etc. of the working medium used by the mold, as well as the environmental conditions in which the mold is placed. By collecting and analyzing the data, the acting force and the environmental influence of the die in use can be more accurately known, and the basis is provided for subsequent analysis and design. And carrying out boundary condition analysis according to the actual working condition data and the material strength parameter data of the die to determine the boundary condition of the die in the using process. These boundary conditions include the manner of support of the mold, constraints, load size, load type, etc. By determining boundary conditions, an appropriate mathematical model can be built and accurate input conditions can be provided for subsequent stress analysis and deformation simulation. And carrying out loading condition analysis according to the actual working condition data of the die and the material strength parameter data to obtain the material loading condition. The loading conditions comprise the information of the magnitude, direction, distribution and the like of external force and acting force applied to the die. By analysing the loading conditions, the characteristics and distribution of the forces acting on the mould can be determined, providing input data for subsequent stress analysis. And carrying out stress analysis on the three-dimensional mold model by using the material boundary condition and the material loading condition to obtain material stress distribution data. By means of stress analysis, stress distribution conditions of all parts of the die under loading conditions can be known, positions and strengths of stress concentration of materials can be found, and basis is provided for strength check and optimization of the die. Meanwhile, the stress analysis can also detect whether the strength of the die meets the design requirement, and is helpful for ensuring that the die cannot be damaged or broken in use. And carrying out deformation simulation on the three-dimensional mold model according to the material loading condition, so as to obtain material deformation data. By simulating the deformation behavior of the material, the deformation condition of the die in the stress process can be predicted, including shape change, displacement, deformation degree and the like. This helps to evaluate the geometric stability and structural rigidity of the mold and provides guidance for the improvement and optimization of the mold. And carrying out data combination on the material stress distribution data and the material deformation data to obtain die material analysis data. The response and performance of the die material under loading conditions can be comprehensively known by integrating and comprehensively analyzing the stress distribution data and the deformation data. The method is favorable for evaluating indexes such as strength, deformation degree, ultimate bearing capacity and the like of the die, and provides basis for the optimal design and service life evaluation of the die.

Optionally, step S2 specifically includes:

step S21: extracting geometric data from the die structure analysis data to obtain die geometric analysis data;

in this embodiment, geometric information of the mold, including size, shape, curved surface features, and the like, is extracted by processing and analyzing the mold structure analysis data. This may be achieved by extracting geometric data from CAD files or making actual measurements using a measurement tool. For example, a three-dimensional scanner is used to scan the mold to obtain point cloud data, and data processing software is used to convert the point cloud into a geometric model.

Step S22: carrying out parameterized modeling according to the geometric analysis data of the die so as to obtain a parameterized geometric model;

in this embodiment, a geometric model with parameterized features is created using parameterized modeling software or CAD tools based on the extracted geometric analysis data. Parametric modeling allows for rapid modification of the shape and size of the mold by adjusting parameter values to meet different design requirements. For example, in three-dimensional modeling software, parameterization tools may be used to create features with adjustable size and shape, such as holes, bosses, chamfers, and the like.

Step S23: extracting material analysis data from the mold structure analysis data, thereby obtaining mold material analysis data;

In this embodiment, the analysis data of the mold structure is processed and analyzed to extract the relevant information of the mold material. This relates to data on material composition, heat treatment history, mechanical properties, etc. These data may be obtained through laboratory tests, material certificates, or an existing database of materials. For example, chemical composition analysis, metallographic observation and mechanical property testing are performed on the mold material to obtain mechanical property parameters of the material.

Step S24: extracting stress distribution data and material deformation data of the die material analysis data to obtain material stress distribution data and material deformation data, and carrying out fitting parameterization on the material stress data and the material deformation data to obtain material parameterization data;

in this embodiment, the mold structure analysis data and the material analysis data are used to extract stress distribution data and material deformation data. And stress analysis and deformation analysis can be performed on the die by using finite element analysis software, so that stress distribution data and deformation data of each point in the die are obtained. These data can help evaluate the stress distribution and deformation of the mold under different loading conditions.

Step S25: and carrying out model parameter iterative optimization on the parameterized geometric model by using the material parameterized data, thereby obtaining the parameterized mold model.

In this embodiment, the parameterized geometric model is subjected to iterative optimization of model parameters by using the mold material performance information obtained from the material parameterized data. By adjusting the parameter values in the model, e.g. changing the size, shape or material, better performance and design requirements are achieved. Optimization may be iteratively improved using specialized optimization software or by manually adjusting parameters. Finally, the optimized parameterized mold model will be the best design to meet the specific requirements.

The invention extracts geometric information in the structural analysis data of the mould, including the shape, the size, the geometric characteristics and the like of the mould. And obtaining geometric analysis data of the die, and providing a basis for subsequent parameterized modeling and optimization. The geometric analysis data of the mould are converted into a parameterized geometric model, so that the shape and the size of the mould can be flexibly changed by adjusting parameters. Parametric modeling provides the ability to quickly iterate and modify the design of a mold, reducing repetitive work in the design process, and improving design efficiency. Material information including physical properties, mechanical properties, etc. of the material is extracted from the mold structure analysis data. Material analysis data of the mold are obtained, and a basis for material properties is provided for subsequent stress distribution and deformation simulation. And extracting stress distribution and material deformation data in the mold material analysis data, and reflecting the stress and deformation conditions of the mold under the loading condition. And fitting parameterization is carried out on the obtained stress data and deformation data, and key parameters are extracted, so that the stress and deformation behaviors of the material can be represented by the parameters, and subsequent analysis and optimization are facilitated. The obtained material parameterized data is applied to a parameterized geometric model to improve the design and performance of the mold by optimizing model parameters. Multiple iterative optimization can be performed, the characteristics of the structure, strength, rigidity and the like of the die are optimized by adjusting the model parameters according to design requirements and constraint conditions, and the improvement and optimization of the performance of the die are realized.

Optionally, step S3 specifically includes:

step S31: extracting die size information from the die structure analysis data, thereby obtaining die size data;

in this embodiment, dimensional information of the mold, such as length, width, height, etc., is extracted by processing and analyzing the mold structure analysis data. This may be accomplished by measuring the dimensions of the various parts of the mold or extracting dimensional data from CAD files. For example, the actual measurement of the mold is performed using a measuring tool, and the relevant dimensional data is recorded.

Step S32: extracting the image of the mold structure data to obtain a mold structure image set, and carrying out pre-processing for edge detection on the mold structure image set to obtain a gray structure image set;

in this embodiment, image extraction is performed on the structural data of the mold, and a structural image set of the mold is obtained. This is to perform image extraction of image data in the mold structure data. The acquired set of structural images is then preprocessed, e.g. noise removed and converted into a gray scale image for subsequent processing.

Step S33: edge point detection is carried out on the gray structure image set, so that an edge point binary image set is obtained;

In this embodiment, edge point detection is performed on the gray structure image set to obtain a binary image set of edge points. Edge point detection is a technique that determines object boundaries by analyzing changes in gray levels in an image. Edge points in the mold structure image may be detected using an edge detection algorithm (e.g., canny algorithm) and converted into a binary image.

Step S34: contour extraction is carried out on the edge point binary image set, so that a closed contour is obtained;

in this embodiment, contour extraction is performed on the edge point binary image set, so as to obtain a closed contour of the mold. By connecting the edge points in the binary image, a closed contour representing the contour of the mold can be obtained.

Step S35: calculating turning point scores of the closed contour through a turning point score calculation formula, so as to obtain turning point score data;

in this embodiment, the turning point score calculation is performed on the closed contour by using a turning point score calculation formula, so as to obtain the score data of the turning point of the mold. Turning points refer to points on the closed contour that change direction significantly, and the degree of deformation and shape characteristics of the mold can be quantified by calculating turning point scores.

Step S36: carrying out turning point marking on the edge point binary image set by using turning point score data so as to obtain mold turning point data;

In this embodiment, turning point score data is used to perform turning point marking on the edge point binary image set to obtain turning point data of the mold. Information marking the turning points can be added to the edge point binary image set according to the threshold of the turning point score.

Step S37: normalizing the die size data and the die turning point data to obtain modeling preparation data;

in this embodiment, the die size data and the die turning point data are normalized to be in a uniform scale range, so as to be used for subsequent modeling preparation.

Step S38: labeling the mold structure image set by using modeling preparation data, so as to obtain a labeling structure image set;

in this embodiment, the modeling preparation data is used to label the mold structure image set. Labeling may include marking the size information of the mold, the location of the inflection points, etc., and associating such information with the set of structural images.

Step S39: and constructing an automatic size marking model according to the marking structure image set.

In this embodiment, an automatic size labeling model is constructed by selecting a suitable model architecture according to the labeling structure image set. Common architectures include Convolutional Neural Networks (CNNs), recurrent Neural Networks (RNNs), and the like. The design of the network needs to take into account the task requirements of image feature extraction and dimensioning. The prepared set of annotation structure images is divided into a training set, a verification set and a test set. The dataset is typically divided proportionally (e.g., 70%, 15%) for training, validating, and evaluating the performance of the model. Training of the model is performed using the training set. By inputting the annotation structure image as a training sample, the model adjusts the weights and parameters by learning the association between features and size annotations in the image. The difference between the model output and the actual size marking can be measured by using a loss function (such as mean square error) in the training process, and the parameter is updated by a back propagation algorithm. The validation set is used to evaluate the performance and generalization ability of the model. By inputting the images in the verification set into the trained model, the difference between the size label and the actual label output by the model is calculated, and performance measurement such as average error, accuracy and the like is performed. And (5) optimizing the super parameters of the model according to the result of the verification set. Super parameters include learning rate, batch size, network layer number, etc. By adjusting these superparameters, the performance and generalization ability of the model can be improved. Automatic size marking of the mold structure image is achieved through training of the model, so that mold size information is automatically extracted and marked.

The invention extracts the size information of the mould from the mould structure analysis data, including the size parameters of length, width, height, diameter and the like. Accurate die size data is obtained, and a foundation is provided for subsequent modeling and size marking. The mold structure data is converted into an image form, so that image processing and analysis are convenient. And carrying out pre-processing for detecting edges on the die structure image set, strengthening edge information, and providing clear image data for subsequent edge point detection and contour extraction. And detecting edge points in the mold structure image, namely contour information of the mold, through an edge point detection algorithm. And obtaining a binary image set of the edge points, converting the edge information into a binary form, and providing a data basis for subsequent contour extraction and turning point calculation. And extracting the closed contour of the die, namely the whole contour of the die appearance, according to the edge point binary image set. After the closed contour of the mold is obtained, further shape analysis and sizing can be performed. Based on the closed contour, turning point scores are calculated for evaluating the curve characteristics of the mold profile. The turning point score data reflects the bending and angle change of the mold curve and provides basis for the subsequent turning point marking and dimension marking. And marking and identifying turning points of the edge point binary image set according to the turning point score data. And obtaining turning point data of the die, namely key turning points of the edge curve change of the die, and using the turning point data for subsequent model modeling and dimension marking. The die size data and the turning point data are normalized and mapped into the same scale range. The normalized modeling preparation data is convenient to uniformly process and analyze, and normalized data input is provided for the subsequent model modeling and labeling model. And marking the die structure image set based on modeling preparation data, and marking key characteristics and size information of the die. And obtaining a structural image set with labeling information, and providing a labeled data set for subsequent automatic size labeling model training. And constructing an automatic size marking model based on the marking structure image set, and using the model for automatically detecting and marking the characteristic size of the die. The automatic size marking model can improve marking efficiency and accuracy, reduce workload of manual marking, and improve automation degree and accuracy of modeling.

Alternatively, the turning point score calculation formula in step S35 is specifically:

；

in the method, in the process of the invention,for turning point score, add->For the horizontal axis coordinate of the viewpoint on the closed contour, +.>For the vertical axis of the closed contour point, +.>For the major length of the closed contour +.>For the minor length of the closed contour +.>For the mean curvature of the closed contour +.>For the relief factor of the observation point on the closed contour, < ->For the perimeter of the closed contour +.>For the flatness factor of the closed contour, +.>Is closed toComplexity coefficients of the contour.

The invention constructs a turning point score calculation formula for calculating the turning point score of the closed contour. The formula fully considers influencing the turning point scoreHorizontal axis coordinates of the observation point on the closed contour of (2)>Longitudinal axis coordinates of the closed contour point +.>Major length of the closed contour +.>Minor length of the closed contour ∈>Mean curvature of the closed contour ∈>Attapulgite coefficient of observation point on closed contour +.>Perimeter of closed contour ∈>Flatness coefficient of the closed contour +.>Complexity factor of the closed contour->A functional relationship is formed:

；

in the middle ofPart is the partial derivative of x, representing the response to changes in the x coordinate. It is used to evaluate the change in the viewpoint in the x-direction. Whereas the molecular main component of the formula +. >The purpose of (a) is to calculate a value based on the position of the observation point and the nature of the curve. />And->Representing the major and minor lengths of the closed contour, these parameters describe the shape of the contour. />A sine function representing the longitudinal position of the observation point on the curve. />A coefficient of concavity and convexity representing the observation point, +.>Is a constant for adjusting the influence of convexity. />This reflects in part the positional relationship of the observation point to the curve and the shape of the curve. />This part calculates the +.>And->Cube roots of square roots of coordinates. It is used to take into account the changing relationship of the position of the observation point and the curve. />This part contains the average curvature and circumferenceLength, flatness, and complexity. It is used to integrate different properties of the curve. The formula calculates the turning point score according to a combination of a series of parameters and functions, and can identify turning points in the profile by quantifying whether the observation point is at the turning point position of the curve. This may be useful for curve analysis and feature extraction. By calculating the turning point score, the position with significant shape change on the curve, i.e. the turning point, can be determined. This helps locate and mark objects or key points in the curve, such as inflection points, corner points, etc., to aid in further analysis and processing. The formula comprehensively considers parameters such as the shape, convexity, circumference, flatness and complexity of the curve, and can provide a turning point evaluation method based on the characteristics.

Optionally, step S4 specifically includes:

step S41: detecting turning points of the parameterized mold model by using an automatic dimension marking model, so as to obtain parameterized turning point data;

in this embodiment, a trained automatic dimensioning model is used to input the structural image of the parameterized mold model into the model. Turning points (corner points) in the mold model are detected by image analysis and feature extraction capabilities of the model. The model outputs coordinates of the turning points and related characteristic information to form a set of parameterized turning point data. For example, for a mold model image, the automatic sizing model may detect coordinates of turning points (100, 200), (300, 150), etc., and provide a characterization of these turning points, such as angle, curvature, etc.

Step S42: performing turning point labeling on the parameterized mold model by using parameterized turning point data so as to obtain a parameterized labeling model;

in this embodiment, the parameterized turning point data set is mapped to the parameterized mold model based on the parameterized turning point data set. And marking the model by utilizing the coordinates and the characteristic information of the parameterized turning points, and associating the position and the attribute information of the turning points with the model. For example, parameterized turning point data is used to add markers to turning points in the mold model, indicating their type, angle, curvature, etc. characteristics. Therefore, the parameterized die model is provided with turning point marks, and subsequent processing and analysis can be better performed.

Step S43: performing size combination selection on the parameterized labeling model by utilizing the automatic size labeling model, so as to obtain an optimal size combination strategy;

in this embodiment, an automatic dimension labeling model is used to perform dimension analysis and combination selection on the parameterized labeling model. By inputting the structural image of the parameterized annotation model, the model can identify and extract dimensional information in the mold model. Depending on the output of the model, a selection of combinations of dimensions may be made, such as selecting a set of combinations of dimensions to meet specific requirements, such as minimum size ranges, maximum size constraints, etc.

Step S44: and carrying out automatic dimension marking on the parameterized mold model through an automatic dimension marking model according to the optimal dimension combination strategy, thereby obtaining automatic dimension marking data.

In this embodiment, the parameterized mold model is automatically resized by the automatic sizing model using the determined optimal sizing combination strategy. The structural image of the parameterized mold model is input into the model, and the model outputs labeling results of each size, and the position and the numerical value of each size in the mold model are identified. The finally obtained automatic dimension marking data can be used for die manufacturing and engineering analysis, and provides basis for subsequent process planning and production. For example, an automatic sizing model may automatically identify the length, width, height, etc. dimensions in a mold model and give corresponding numerical labels, such as 100mm in length, 50mm in width, 200mm in height, etc.

According to the invention, the turning points of the parameterized mold model are detected through the automatic dimension marking model, so that the important curve change points in the mold design can be found and converted into parameterized turning point data. This will facilitate further mould design and analysis work. The key characteristic points can be marked and recorded in the mould model by marking the turning points of the parameterized mould model by using parameterized turning point data, so that a parameterized marking model is obtained. This will facilitate mold design, visualization, and subsequent size combination selection. And the automatic dimension marking model is used for dimension combination selection of the parameterized marking model, so that an optimal dimension combination strategy can be found according to design requirements and constraint conditions. This will help the designer to quickly determine the dimensional parameters of the mold, improving the efficiency and accuracy of the design. According to the optimal size combination strategy, the automatic size marking model is utilized to automatically size mark the parameterized mold model, and the marked size data can be automatically obtained. The manual labeling workload is greatly reduced, and the labeling speed and accuracy are improved.

Optionally, step S5 specifically includes:

step S51: acquiring production size data of a die;

In this embodiment, the dimensional data in the actual production process of the mold is obtained by measuring, scanning or other means. This may include the length, width, height, and other relevant dimensional parameters of the mold. For example, the die is measured in dimensions one by one using a measuring tool such as a caliper, a measuring instrument, or the like, to obtain actual production dimension data such as a length of 100mm, a width of 50mm, a height of 200mm, or the like.

Step S52: performing size matching on the parameterized mold model by utilizing mold production size data so as to obtain practical applicable size data;

the acquired mold production dimension data is applied to the parameterized mold model in this embodiment. And determining the size of the model corresponding to the size data measured in the actual production process by matching with the size parameters in the model. For example, for a length parameter in a parametric mold model, the length measured from actual production is 100mm, and the value is applied to the corresponding parameter in the model to match the length of the model to the actual production size.

Step S53: performing size marking on the parameterized mold model by using the actual applicable size data so as to obtain the actual marked size data;

In this embodiment, the parameterized mold model is labeled according to the actual applicable size data. And correlating the actual measured dimension data with corresponding dimension parameters in the mold model, and marking. For example, on a graphical interface of a mold model, the actual applicable dimensional data is marked on the corresponding location or part of the mold so that the actual dimensional information can be clearly understood in subsequent operations and analysis.

Step S54: and carrying out size error self-adaptive correction on the automatic size marking data according to the actual size marking data, thereby obtaining the adjusted size marking data.

In this embodiment, based on the actual dimension data, the dimension data previously marked by using the automatic dimension marking model is adaptively adjusted. And comparing the difference between the actual dimension marking data and the automatic dimension marking data, and correspondingly adjusting the automatic dimension marking data. For example, if the actual sizing data differs or does not agree with the auto-sizing data, the auto-sizing data may be supplemented, modified, or revised to stay in agreement with the actual sizing. The adjusted marked size data is the adjusted marked size data, and can be used for further process planning and production operation.

The invention can obtain the real production size data by obtaining the size information of the actual template produced by the mould. The data are obtained by measuring the actual template or die product, reflecting the possible factors of material variation, processing errors, etc. in the actual production process. By matching the mold production dimension data with the parameterized mold model, the actual applicable dimension parameters of the mold can be determined. This helps to ensure that the mold design is consistent with the actual mold size, reducing dimensional deviations due to production variations, and thus improving product quality and performance. And dimension marking is carried out on the parameterized mold model by using the practically applicable dimension data, and the practically applicable dimension information is added into the mold design. This helps the designer to understand the actual mold dimensions more clearly, thereby facilitating handling and adjustment of the mold during mold maintenance and modification. Potential dimensional differences can be detected by comparing the actual dimensional data with automatically-scaled dimensional data. The automatic dimensioning can then be automatically or manually adjusted according to the actual dimensioning data to ensure that the dimensioning is consistent with the actual requirements. This helps to ensure accuracy and reliability of the mold design.

Optionally, step S54 specifically includes:

performing alignment comparison on the actual dimension marking data and the automatic dimension marking data, thereby obtaining dimension error data;

in this embodiment, the actual size data is obtained by measuring or scanning the mold pieces actually produced. And comparing and aligning the actual size data with the automatically marked size data. By calculating the difference or deviation, dimension error data representing the difference between the actual dimension and the marked dimension is obtained.

Carrying out structural error extraction on the parameterized mold model by utilizing the size difference data so as to obtain structural error data;

the difference between the actual size data and the resized data is used to extract structural errors based on the parameterized mold model in this embodiment. And comparing the application condition of the actual size data and the marked size data in the mold model, and determining the position and the range of the structural error. And extracting structural error data by analyzing the mode and the trend of the size difference, and describing the deviation condition of the die model in terms of size.

Carrying out maximum adjustment numerical calculation on the structural error data through a size error correction calculation formula, thereby obtaining a size error correction numerical value;

In this embodiment, an appropriate calculation formula for correcting the dimensional error is formulated, and the severity of the structural error and the design requirement are considered. Based on the structural error data, a maximum adjustment value for each dimension parameter is calculated, indicating that the maximum dimension correction is performed within the allowable range.

The calculation formula of the dimensional error correction is specifically as follows:

in the method, in the process of the invention,correction value for dimensional error, +.>Is the size error value +.>For the structural length of dimensional error, +.>The value to be adjusted for dimensional errors, +.>Evaluating index coefficient for error, < >>Is a correction factor of the size error structure, +.>Curvature for dimensional error structure, +.>Is the concave-convex coefficient of the size error structure, +.>For the structural positional offset coefficient, +.>Is the total number of size error areas;

the invention constructs a size error correction calculation formula for carrying out maximum adjustment numerical calculation on structural error data. The formula fully considers the influence size error correction valueDimension error value +.>Dimension error structural length +.>The value of the dimensional error to be adjusted +.>Error evaluation index coefficient->Correction factor of size error structure +.>Curvature of dimension error structure>The relief coefficient of the size error structure +. >，/>Total number of size error regions->A functional relationship is formed:

wherein the method comprises the steps ofThis is partly the result of calculating the ratio of the error assessment index coefficient and the length of the dimension error structure. First, it integrates the dimensional error, wherein +.>Is the size error value,/">Is the structural positional offset coefficient,/>Is the total number of size error regions. Then, the integral result is subjected to an squaring operation and is combined with +.>(natural logarithm of sum of the structural length of the dimensional error and the adjustment value). />This part is the result of the calculation of the correction factor, curvature and asperity coefficient of the dimensional error structure. First, it calculates +.>(error evaluation index coefficient +.>Power) and then base the logarithm of 2. Next, calculate +.>(dimensional error structural curvature +.>Power of four). Finally, cube root operation is carried out on the two results. The formula considers a plurality of parameters such as an integral result of the size error, an error evaluation index coefficient, a size error structural length, a correction factor, curvature, a concave-convex coefficient and the like. By taking these factors into consideration, the dimensional errors can be more accurately assessed and adjusted, thereby improving the accuracy of the mold. The parameters in the formula can be adjusted according to specific conditions, so that the method is suitable for different types of dimensional errors and die structures. This makes the formula somewhat versatile and applicable to a variety of different engineering and manufacturing fields. The formula contains the operation and combination of a plurality of parameters, and the different aspects of the dimensional error are comprehensively considered. This comprehensive approach allows for a more comprehensive assessment of dimensional errors and provides corresponding adjustment values, thereby improving dimensional accuracy of the mold in a number of ways. The size error correction value in the formula can guide the adjustment of the size of the die to overcome the size error and improve the accuracy of the die. The adjusting value calculated according to the formula can be used for purposefully matching the mould The die is adjusted to reduce dimensional errors to the greatest extent and improve the performance of the die and the quality of the workpiece.

And performing size correction on the automatic size marking data by using the size error correction value, thereby obtaining the adjusted size marking data.

In this embodiment, the size error correction value is applied to the automatically marked size data, and the size is adjusted accordingly. And increasing or decreasing the automatically marked size value according to the positive and negative directions of the maximum adjustment value so as to enable the automatically marked size value to approach the actual size and the required adjustment size.

The invention can determine the difference between the actual dimension data and the automatic dimension data, namely the dimension error by comparing the two data. This helps to understand the accuracy of the automated labeling and to identify possible dimensional deviations during design and production. By analyzing the dimensional difference data, specific portions and factors that cause dimensional errors can be determined. This helps to extract structural information of the dimensional errors, i.e. to understand the distribution and origin of the dimensional errors in the mold structure. By applying the dimensional error correction calculation formula, the maximum adjustment value in the structural error data can be determined. This helps to determine the upper limit of the dimensional correction, ensuring the feasibility and rationality of dimensional error control during mold design and production. By applying the size error correction value, the automatically marked size data can be correspondingly adjusted, and corrected to the size standard meeting the actual requirement. This helps to ensure dimensional accuracy in mold design and production and to improve product quality and performance.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The automatic dimension marking method for the parameterized assembly design of the three-dimensional die structure is characterized by comprising the following steps of:

Step S3, including:

the turning point score calculation formula is as follows:

in the method, in the process of the invention,for turning point score, add->For the horizontal axis coordinate of the viewpoint on the closed contour, +.>To close the vertical axis coordinates of the contour points,for the major length of the closed contour +.>For the minor length of the closed contour +.>For the mean curvature of the closed contour +.>For the relief factor of the observation point on the closed contour, < ->For the perimeter of the closed contour +.>For the flatness factor of the closed contour, +.>Complexity coefficients for the closed contour;

step S39: constructing an automatic size marking model according to the marking structure image set;

2. The method for automatically dimensioning a three-dimensional mold structure parametric package design according to claim 1, wherein step S1 is specifically:

3. The method for automatically sizing a three-dimensional mold structure parametric package design according to claim 2, wherein step S14 specifically comprises:

4. The method for automatically sizing a three-dimensional mold structure parametric package design according to claim 2, wherein step S15 specifically comprises:

Acquiring actual working condition data of a die;

5. The method for automatically dimensioning a three-dimensional mold structure parametric package design according to claim 1, wherein step S2 is specifically:

6. The method for automatically dimensioning a three-dimensional mold structure parametric package design according to claim 1, wherein step S4 is specifically:

7. The method for automatically dimensioning a three-dimensional mold structure parametric package design according to claim 1, wherein step S5 is specifically:

step S51: acquiring production size data of a die;

8. The method of automatic sizing for three-dimensional mold structure parametric package design as recited in claim 7, wherein step S54 is specifically: